Yazar "Mercan, Hatice" seçeneğine göre listele

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    Application of artificial intelligence techniques for heat exchanger predictions in food industry
    (Elsevier, 2024) Öztuna Taner, Öznur; Mercan, Hatice; Çolak, Andaç Batur; Radulovic, Jovana; Taner, Tolga; Dalkılıç, Ahmet Selim
    Heat exchangers (HEXs) are deployed in diverse engineering applications, such as cooling and refrigeration systems; power plants; and automotive, chemical, textile, and food industries. Understanding the principles and fluid-to-fluid heat exchange geometry can be complex. Researchers usually apply the first and second laws of thermodynamics to conduct numerical, analytical, and experimental techniques on HEXs. Experimental approaches tend to be costlier due to setup expenses, while theoretical and numerical analyses rely heavily on assumptions and complex equations. To address these challenges, artificial intelligence (AI) models have emerged as a promising solution for modeling, optimization, and performance estimation of thermal systems employing HEXs. In the last 30 years, AI-based approaches have gained widespread adoption in thermal analysis of HEXs, building upon past research. Three main types of thermal analysis have been reported: single-phase flow, two-phase flow, and machine learning-based physical property evaluation. AI approaches have proven effective in estimating crucial HEX parameters like pressure drop (?P), heat transfer coefficient (h), friction factor (f), and Nusselt number (Nu). They have also demonstrated success in assessing phase change characteristics during fluid boiling and condensation processes, as well as identifying two-phase flows. Despite these advancements, it is emphasized that more work remains to fully harness AI’s potential for thermal analysis of HEXs. As AI gains traction, it presents itself as a valuable technology for enhancing the study of HEXs with satisfactory results.
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    Öğe
    Thermophysical and rheological properties of unitary and hybrid nanofluids
    (Elsevier, 2022) Mercan, Hatice; Çelen, Ali; Taner, Tolga
    Understanding the thermophysical properties of the working fluids in a heat transfer system is essential for improving the thermal performance of heat transfer equipment. Clarification of the complicated and entangled mechanisms of the thermophysical properties and the operating conditions need detailed experimental and modeling efforts. Nanofluids, although showing the desired improvements in the thermophysical properties, are more sensitive to operating conditions compared to traditional working fluid. The working fluid can be a single fluid, its mixtures, as well as a nanosuspension. Ethylene glycol, ethanol, water, motor oil, ammonia, and halogenated hydrocarbons are some instances of working fluids in thermal systems. This book chapter reveals the thermophysical and rheological properties of unitary and hybrid nanofluids. The behavior of nanofluids used as working fluid in a thermal system affects the thermophysical and rheological properties of the flow within the system, as well as the thermal performance of the overall system. Nanofluids are complex dispersed suspensions, and the stability, chemical and thermal compatibility of nanoparticles must be ensured during the preparation procedures and during the operation conditions. This chapter includes thermal conductivity, heat capacity, density, and viscosity alteration of nanofluids under different operating conditions such as nanoparticle type, size, temperature, and concentration. The application of nanofluids is very promising and can be a good candidate for new-generation working fluids in industrial applications. Recently the application of hybrid nanofluids has become more popular since they enhance thermophysical properties according to the selection of compatible two nanoparticle types compared to unitary nanofluids. The thermal conductivity, the viscosity and the density of the nanofluids rise with increasing concentration and alter with base fluid and nanoparticle type. Temperature increase augments the thermal conductivity but it reduces the viscosity and density of the nanoblend. Specific heat capacity of the nanofluid decreases with increasing concentration and it is directly proportional to the temperature. This study contains a thorough digest of recent novel works of researchers in the nanofluid field and aims to model and encourage the research in the academic community, as well as to lead to the more efficient use of thermal applications, industrial processes, and new technologies.